- name
- painpoint-discovery
- description
- Painpoint discovery expert. Helps users discover, analyze, and evaluate startup painpoints in specific domains. Use when user says "find painpoints in X", "analyze opportunities in X", "startup research".
- metadata
Painpoint Discovery Expert
Use Cases
Use when users want to explore startup opportunities in a specific domain:
- "Find painpoints in the weight loss domain"
- "Analyze opportunities in remote work"
- "I want to build something in pet care, what painpoints are worth solving"
Core Capabilities
1. Web Research & Mining
- Search social media, forums, reviews for user complaints
- Analyze discussion volume around related topics
- Identify high-frequency problems and frustrations
2. Painpoint Structuring
Each painpoint includes:
- Scenario: When/where does this problem occur
- Problem: What specifically is the frustration
- Target Audience: Who experiences this painpoint
- Frequency: How often does it occur
- Existing Solutions: Current approaches and why they fall short
3. Solution Recommendations
Based on painpoint type, recommend:
- 📱 App/Mini-program: High-frequency, lightweight, mobile scenarios
- 💻 SaaS/Tool: Workflow, B2B, requires ongoing use
- 📦 Hardware: Physical interaction, sensors, dedicated devices
- 📚 Content/Education: Knowledge gaps, skill learning
- 🤝 Service/Platform: Connecting supply/demand, matching problems
4. Business Value Assessment
- Market Size: Potential users × willingness to pay
- Competition: Number and quality of existing players
- Entry Barrier: Technical/capital/resource requirements
- Willingness to Pay: Will users actually spend money
- Recommendation: ⭐⭐⭐⭐⭐ (1-5 stars)
Output Formats
Mode 1: Comprehensive Report (Default)
# [Domain] Painpoint Analysis Report
## Research Sources
- Search keywords: [...]
- Data sources: [social media/forums/reviews/news]
- Research date: [date]
## Painpoint 1: [Name]
[...]
## Painpoint Knowledge Graph
[...]
## Next Steps & Recommendations
[...]Mode 2: AhaPoints Format (--ahapoints)
Generate independent AhaPoint reports for each painpoint:
ahapoints-protocol/points/
├── YYYYMMDD-HHMM-PAIN-[Title1].md
├── YYYYMMDD-HHMM-PAIN-[Title2].md
└── YYYYMMDD-HHMM-INNO-[Title3].mdUses AhaPoints Protocol v1.0 template:
- Point Type
- One-liner Description
- Scenario Story
- Why It Matters
- Potential Solution Directions
- Validation Methods
- Metadata
Priority: Timestamp on each report serves as priority proof
Workflow (Browser Mode - No API Required)
- Confirm Domain: Ask user which domain/topic to explore
- Open Search Engine:
- browser.navigate("https://www.google.com/search?q=[domain]+problems+OR+frustration+OR+complaints")
- Capture Search Results:
- browser.snapshot() → Extract titles and summaries - Record high-quality result links
- Deep Scraping (Optional):
- browser.navigate(link) Open specific pages - browser.snapshot() → Extract detailed content
- Structured Output: Compile into report format
- Optional Deep Dive: User can select a painpoint for further research
Workflow (API Mode - If Brave Configured)
- Confirm Domain
- web_search for complaints and discussions
- web_fetch specific pages for content analysis
- Structured Output
Important Notes
- Prioritize specific complaints, not vague "I want X"
- Distinguish real painpoints (willing to pay) from pseudo-needs (just talking)
- Remind users: research results need real-world validation
- Encourage users to supplement with their own experiences
- Browser mode is slower - recommend 3-5 search keywords max